摘要
针对轨道交通氢锂混合动力系统中的多目标协同优化问题,本文提出了一种基于多目标人工蜂鸟算法(Multi-Objective Artificial Hummingbird Algorithm,MOAHA)的能量管理策略。通过构建等效氢耗、燃料电池寿命退化及锂电池容量衰退的多目标优化模型,采用MOAHA算法求解帕累托最优解集,避免人为设定权重的主观偏差。结果表明,所提出方法可获得涵盖“燃料经济优先”至“系统寿命优先”的连续非劣解集,实现多目标的自适应协同优化。该策略可提升系统的综合能效与运行可靠性,为氢锂混合动力系统提供兼顾燃料经济性和系统耐久性的策略。
To address the multi-objective collaborative optimization in rail transit hydrogen-lithium hybrid power systems,this paper proposes an energy management strategy based on the Multi-Objective Artificial Hummingbird Algorithm(MOAHA).A multi-objective optimization model integrating equivalent hydrogen consumption,fuel cell lifespan degradation,and lithium battery capacity degradation is established.The MOAHA resolves Pareto optimal solutions,eliminating subjective bias from manual weight assignment.Results demonstrate that the method generates continuous non-dominated solutions spanning from"fuel economy priority"to"system lifespan priority",achieving adaptive multi-objective optimization.This strategy enhances comprehensive energy efficiency and operational reliability,providing balanced solutions for fuel economy and system durability in hydrogen-lithium hybrid power systems.
作者
黄文杰
Huang WenJie(College of Transportation and Electrical Engineering,Hunan University of Technology,Zhuzhou 412007,China;Engineering Research Center for New Energy System of Railway Industry,Zhuzhou 412001,China)
出处
《船电技术》
2025年第12期84-89,共6页
Marine Electric & Electronic Engineering
基金
新型能源系统铁路行业工程研究中心开放课题项目:05-25-9003-0资助。
关键词
混合动力系统
多目标优化
能量管理策略
人工蜂鸟算法
hybrid power system
multi-objective optimization
energy management strategy
artificial hummingbird algorithm
